Big Data Analysis as Self-Service SaaS for Everyone? That’s Quantivo’s Plan


Big data is widely seen as the next big thing in marketing and customer relations by large enterprises. Technologies like Cloudera to Amazon Elastic MapReduce are mostly based on the open-source Hadoop database technology developed originally by Yahoo and Google to organize and analyze very large volumes of unstructured and semi-structured data. The technology offers ways to capture, structure, and analyze huge volumes of data from the Internet on consumer social activities.

SMBs, however, have been pretty much locked out of the big data revolution by the cost. This big data analysis is providing important insights. But, as Amrit Williams, CTO for big data analysis company Quantivo says questions can be easy to ask but difficult to be coded for data analysis. And because the technologies involved are new, the skill sets are rare. Therefore, most big data is being provided as a specialized service by companies like Cloudera, who sell expensive services primarily to large enterprises.

Next year Quantivo plans to turn the market on its head by offering a self-service SaaS big data analysis service modeled after using a unique drag-and-drop interface that makes it easy for business users to create their queries themselves.. “I hate the enterprise sales process,” says Williams. “It’s bulky, big, unpredictable, and the decisions people make tend to be self-destructive. “When I was at Big Fix before its acquisition we spent a half million dollars with and I don’t know how many hundreds of thousands with Success Factors, and we never once saw a sales person. We were empowered to educate ourselves on that tool, deploy that tool, and start using it and get provisioned without involving sales.”

That is exactly the model that Quantivo hopes to follow with its SaaS big data analysis service for providing detailed, data-driven answers to core marketing questions. Among other things it analyzes incoming unstructured data upfront to identify the hidden structures that become the basis for a data schema. Then the system dedupes the data and compresses it in a way that allows it to then act on the data without decompressing, saving huge amounts of space and compute resources uncompressing data each time it is used.

This allows it to handle billions or hundreds-of-billions of references much faster than Hadoop-based technologies, that handle high volumes of unstructured data in a schema-less approach that cuts data ingestion time but makes analysis much slower and more complex.

Quantivo uses this technology to provide analysis of marketing questions to find unexpected answers. So for instance an online game company asked what the greatest follow-on sales opportunities were for customers who came to their site initially in response to Google ads offering Xbox 360 sports-action games. They had been trying to cross-sell these customers with first-person shooters and boxing or wrestling games with limited success. The analysis of what these customers actually did on their subsequent visits to the site showed that these users were four-times more likely to be attracted to music games than anything else. Fighting only registered at 2.9X, and personal shooters and wrestling games didn’t even make the list.

So far Quantivo, like its competitors, has sold its services through enterprise-style sales scenarios. Williams says they will maintain that and the sales organization that supports those customers. But in 2012 it intends to take advantage of that intuitive analysis interface to create a pure self-service SaaS offering, in which prospects can try out the technology in a digital sandbox and, if they like it, upload their own data or send it on media to Quantivo for upload and then do their own interactive analysis.

“We hope to reach an entirely new, underserved part of the market with this,” he said.

Services Angle:

Quantivo plans to make big data available to medium-sized companies and others who need insights into customer behavior to allow them to improve their marketing strategy but lack the budget to pay for the expensive big data services of other companies. This can provide a so-far unique opportunity for these companies to gain an equal footing with the big enterprises in the new data-driven marketing arena.